Second-order factor analysis applied to data from European Social Survey

Specialeforsvar ved Sophie Sustmann Helledie

Titel: Second-order factor analysis applied to data from European Social Survey

Abstract: Factor Analysis is a statistical method to describe the relationships among correlated variables using a lower number of unobserved, latent variables called factors. The method is mostly used in behavioural science as it processes multivariate data sets such as large social surveys. As surveys measure complex concepts, factor analysis helps conceptualizing data due to the lower-dimension summery created by the factors.

The method is applied on the European Social Surveys Climate Module from 2016 on Swedish data only. The approach includes both first-order and second-order factor analysis. The European Social Survey (ESS) has specified their conceptual framework of the Climate Module which presents what the questionnaire is expected to cover. The ESS framework provides (1) the number of components, (2) which questions are related to which component and (3) whether or not these components are correlated. This easily translates to a factor model hypothesis. That premise was evaluated by using Confirmatory Factor Analysis with Maximum Likelihood Estimation (MLE).

The results of the first-order analysis revealed that many of the questions were in fact not highly correlated to one another, hence not related to the expected component. As a result, the firstorder model was not consistent with the observed data. A second-order analysis did therefore not provide meaningful information in regards to further interpretation of data. Consequently, the ESS framework was de-prioritized and various factor models very developed and analysed by the same MLE techniques. The models were developed by applying both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). The common result was that the models were more consistent with the observed data compared to the ESS model. The analyses exposed that the questions in the Climate Module were not as related as ESS expected. As a consequence, the developed models only described approximately two thirds of the data set. The most valid model was chosen by several goodness-of-fit statistics. A less complex first-order model with four underlying factors was preferred. The preferred second-order model became a re-parametrization of the first-order model, thus only contributing with greater interpretation of the data set. The conclusion of the analyses is that the Climate Module in Sweden is a measure of Swedes’ energy security concerns, climate change beliefs, green energy preferences and lastly efficacy beliefs in reducing climate change. Furthermore, a second-order analysis summarized the three first mentioned as an overall measure of the Swedes’ environmental conservations. Since these results are only based on approximately two thirds of the questionnaire, we finally conclude that the European Social Survey Climate Module questions were not successfully formulated to apply factor analysis.